{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "spark = SparkSession.builder.appName('k_means').getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyspark\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pyspark.sql.functions import * \n",
    "from pyspark.sql.types import *\n",
    "from pyspark.sql.functions import rand, randn\n",
    "from pyspark.ml.clustering import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=spark.read.csv('iris_dataset.csv',inferSchema=True,header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 5)\n"
     ]
    }
   ],
   "source": [
    "print((df.count(),len(df.columns)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- sepal_length: double (nullable = true)\n",
      " |-- sepal_width: double (nullable = true)\n",
      " |-- petal_length: double (nullable = true)\n",
      " |-- petal_width: double (nullable = true)\n",
      " |-- species: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+-----------+------------+-----------+----------+\n",
      "|sepal_length|sepal_width|petal_length|petal_width|species   |\n",
      "+------------+-----------+------------+-----------+----------+\n",
      "|5.5         |2.6        |4.4         |1.2        |versicolor|\n",
      "|4.5         |2.3        |1.3         |0.3        |setosa    |\n",
      "|5.1         |3.7        |1.5         |0.4        |setosa    |\n",
      "|7.7         |3.0        |6.1         |2.3        |virginica |\n",
      "|5.5         |2.5        |4.0         |1.3        |versicolor|\n",
      "|6.3         |2.3        |4.4         |1.3        |versicolor|\n",
      "|6.2         |2.9        |4.3         |1.3        |versicolor|\n",
      "|6.3         |2.5        |4.9         |1.5        |versicolor|\n",
      "|4.7         |3.2        |1.3         |0.2        |setosa    |\n",
      "|6.1         |2.8        |4.0         |1.3        |versicolor|\n",
      "+------------+-----------+------------+-----------+----------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.orderBy(rand()).show(10,False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select('species').distinct().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-----+\n",
      "|species   |count|\n",
      "+----------+-----+\n",
      "|virginica |50   |\n",
      "|setosa    |50   |\n",
      "|versicolor|50   |\n",
      "+----------+-----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.groupBy('species').count().orderBy('count',ascending=False).show(10,False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.linalg import Vectors\n",
    "from pyspark.ml.feature import VectorAssembler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_cols=['sepal_length', 'sepal_width', 'petal_length', 'petal_width']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform all features into a vector using VectorAssembler\n",
    "vec_assembler = VectorAssembler(inputCols = input_cols, outputCol='features')\n",
    "final_data = vec_assembler.transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With K=2\n",
      "Within Set Sum of Squared Errors = 31.377209665335272\n",
      "------------------------------------------------------------\n",
      "With K=3\n",
      "Within Set Sum of Squared Errors = 31.377209665335272\n",
      "------------------------------------------------------------\n",
      "With K=4\n",
      "Within Set Sum of Squared Errors = 31.377209665335272\n",
      "------------------------------------------------------------\n",
      "With K=5\n",
      "Within Set Sum of Squared Errors = 31.377209665335272\n",
      "------------------------------------------------------------\n",
      "With K=6\n",
      "Within Set Sum of Squared Errors = 31.377209665335272\n",
      "------------------------------------------------------------\n",
      "With K=7\n",
      "Within Set Sum of Squared Errors = 31.377209665335272\n",
      "------------------------------------------------------------\n",
      "With K=8\n",
      "Within Set Sum of Squared Errors = 31.377209665335272\n",
      "------------------------------------------------------------\n",
      "With K=9\n",
      "Within Set Sum of Squared Errors = 31.377209665335272\n",
      "------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "errors=[]\n",
    "\n",
    "for k in range(2,10):\n",
    "    kmeans = KMeans(featuresCol='features',k=k)\n",
    "    model = kmeans.fit(final_data)\n",
    "    intra_distance = model.computeCost(final_data)\n",
    "    errors.append(intra_distance)\n",
    "    print(\"With K={}\".format(k))\n",
    "    print(\"Within Set Sum of Squared Errors = \" + str(wssse))\n",
    "    print('--'*30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff365c0ee48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_number = range(2,10)\n",
    "plt.scatter(cluster_number,errors)\n",
    "plt.xlabel('Number of Clusters (K)')\n",
    "plt.ylabel('SSE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Selecting k =3 for kmeans clustering\n",
    "kmeans = KMeans(featuresCol='features',k=3,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = kmeans.fit(final_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-----+\n",
      "|prediction|count|\n",
      "+----------+-----+\n",
      "|         1|   50|\n",
      "|         2|   38|\n",
      "|         0|   62|\n",
      "+----------+-----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model.transform(final_data).groupBy('prediction').count().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=model.transform(final_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal_length',\n",
       " 'sepal_width',\n",
       " 'petal_length',\n",
       " 'petal_width',\n",
       " 'species',\n",
       " 'features',\n",
       " 'prediction']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+----------+-----+\n",
      "|   species|prediction|count|\n",
      "+----------+----------+-----+\n",
      "| virginica|         2|   14|\n",
      "|    setosa|         0|   50|\n",
      "| virginica|         1|   36|\n",
      "|versicolor|         1|    3|\n",
      "|versicolor|         2|   47|\n",
      "+----------+----------+-----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions.groupBy('species','prediction').count().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>features</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>[5.4, 3.9, 1.7, 0.4]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>[5.7, 3.0, 4.2, 1.2]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "      <td>[6.4, 2.8, 5.6, 2.2]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>[6.4, 2.8, 5.6, 2.1]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>setosa</td>\n",
       "      <td>[5.1, 3.3, 1.7, 0.5]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width     species  \\\n",
       "5             5.4          3.9           1.7          0.4      setosa   \n",
       "95            5.7          3.0           4.2          1.2  versicolor   \n",
       "132           6.4          2.8           5.6          2.2   virginica   \n",
       "128           6.4          2.8           5.6          2.1   virginica   \n",
       "23            5.1          3.3           1.7          0.5      setosa   \n",
       "\n",
       "                 features  prediction  \n",
       "5    [5.4, 3.9, 1.7, 0.4]           0  \n",
       "95   [5.7, 3.0, 4.2, 1.2]           2  \n",
       "132  [6.4, 2.8, 5.6, 2.2]           1  \n",
       "128  [6.4, 2.8, 5.6, 2.1]           1  \n",
       "23   [5.1, 3.3, 1.7, 0.5]           0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas_df = predictions.toPandas()\n",
    "pandas_df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff365b01ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_vis = plt.figure(figsize=(15,10)).gca(projection='3d')\n",
    "cluster_vis.scatter(pandas_df.sepal_length, pandas_df.sepal_width, pandas_df.petal_length, c=pandas_df.prediction,depthshade=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
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